{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/three-dimensional-segmentation-of-vesicular","title":"Three-Dimensional Segmentation of Vesicular Networks of Fungal Hyphae in Macroscopic Microscopy Image Stacks","arxiv_id":"1704.02356","date":"2017-04-07","proceeding":null,"authors":["P. Saponaro","W. Treible","A. Kolagunda","S. Rhein","J. Caplan","C. Kambhamettu","R. Wisser"],"abstract":"Automating the extraction and quantification of features from\nthree-dimensional (3-D) image stacks is a critical task for advancing computer\nvision research. The union of 3-D image acquisition and analysis enables the\nquantification of biological resistance of a plant tissue to fungal infection\nthrough the analysis of attributes such as fungal penetration depth, fungal\nmass, and branching of the fungal network of connected cells. From an image\nprocessing perspective, these tasks reduce to segmentation of vessel-like\nstructures and the extraction of features from their skeletonization. In order\nto sample multiple infection events for analysis, we have developed an approach\nwe refer to as macroscopic microscopy. However, macroscopic microscopy produces\nhigh-resolution image stacks that pose challenges to routine approaches and are\ndifficult for a human to annotate to obtain ground truth data. We present a\nsynthetic hyphal network generator, a comparison of several vessel segmentation\nmethods, and a minimum spanning tree method for connecting small gaps resulting\nfrom imperfections in imaging or incomplete skeletonization of hyphal networks.\nQualitative results are shown for real microscopic data. We believe the\ncomparison of vessel detectors on macroscopic microscopy data, the synthetic\nvessel generator, and the gap closing technique are beneficial to the image\nprocessing community.","url_abs":"http://arxiv.org/abs/1704.02356v1","url_pdf":"http://arxiv.org/pdf/1704.02356v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"three-dimensional-segmentation-of-vesicular","repo_url":"https://github.com/drmaize/compvision","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}